Justification, margin values, and analysis populations for oncologic noninferiority and equivalence trials: a meta-epidemiological study
Bibliographic record
Abstract
BACKGROUND: Noninferiority and equivalence trials evaluate whether an experimental therapy's effect on the primary endpoint is contained within an acceptable margin compared with standard of care. The reliability and impact of this conclusion, however, is largely dependent on the justification for this design, the choice of margin, and the analysis population used. METHODS: A meta-epidemiological study was performed of phase 3 randomized noninferiority and equivalence oncologic trials registered at ClinicalTrials.gov. Data were extracted from each trial's registration page and primary manuscript. RESULTS: We identified 65 noninferiority and 10 equivalence trials that collectively enrolled 61 632 patients. Of these, 61 (81%) trials demonstrated noninferiority or equivalence. A total of 65 (87%) trials were justified in the use of a noninferiority or equivalence design either because of an inherent advantage (53 trials), a statistically significant quality-of-life improvement (6 trials), or a statistically significant toxicity improvement (6 trials) of the interventional treatment relative to the control arm. Additionally, 69 (92.0%) trials reported a prespecified noninferiority or equivalence margin of which only 23 (33.3%) provided justification for this margin based on prior literature. For trials with time-to-event primary endpoints, the median noninferiority margin was a hazard ratio of 1.22 (range = 1.08-1.52). Investigators reported a per-protocol analysis for the primary endpoint in only 28 (37%) trials. CONCLUSIONS: Although most published noninferiority and equivalence trials have clear justification for their design, few provide rationale for the chosen margin or report a per-protocol analysis. These findings underscore the need for rigorous standards in trial design and reporting.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | medium |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.368 | 0.599 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".